Overview

Dataset statistics

Number of variables18
Number of observations18448
Missing cells39534
Missing cells (%)11.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 MiB
Average record size in memory180.6 B

Variable types

Numeric11
Categorical6
Unsupported1

Alerts

name has a high cardinality: 13413 distinct valuesHigh cardinality
host_name has a high cardinality: 4863 distinct valuesHigh cardinality
last_review has a high cardinality: 2379 distinct valuesHigh cardinality
license has a high cardinality: 5510 distinct valuesHigh cardinality
id is highly overall correlated with minimum_nights and 1 other fieldsHigh correlation
latitude is highly overall correlated with neighbourhoodHigh correlation
longitude is highly overall correlated with neighbourhoodHigh correlation
minimum_nights is highly overall correlated with id and 3 other fieldsHigh correlation
number_of_reviews is highly overall correlated with reviews_per_month and 1 other fieldsHigh correlation
reviews_per_month is highly overall correlated with id and 4 other fieldsHigh correlation
availability_365 is highly overall correlated with minimum_nights and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly overall correlated with minimum_nights and 2 other fieldsHigh correlation
neighbourhood is highly overall correlated with latitude and 1 other fieldsHigh correlation
neighbourhood_group has 18448 (100.0%) missing valuesMissing
last_review has 4995 (27.1%) missing valuesMissing
reviews_per_month has 4995 (27.1%) missing valuesMissing
license has 11094 (60.1%) missing valuesMissing
id has unique valuesUnique
neighbourhood_group is an unsupported type, check if it needs cleaning or further analysisUnsupported
number_of_reviews has 4995 (27.1%) zerosZeros
availability_365 has 8708 (47.2%) zerosZeros
number_of_reviews_ltm has 11975 (64.9%) zerosZeros

Reproduction

Analysis started2023-07-17 19:15:21.476322
Analysis finished2023-07-17 19:16:07.944476
Duration46.47 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct18448
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1798449 × 1017
Minimum61721
Maximum9.0714141 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:08.133467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum61721
5-th percentile5843009.2
Q119488084
median36615792
Q36.3868683 × 1017
95-th percentile8.6548554 × 1017
Maximum9.0714141 × 1017
Range9.0714141 × 1017
Interquartile range (IQR)6.3868683 × 1017

Descriptive statistics

Standard deviation3.4968245 × 1017
Coefficient of variation (CV)1.6041621
Kurtosis-0.87006856
Mean2.1798449 × 1017
Median Absolute Deviation (MAD)20130672
Skewness1.0225948
Sum-1.2290859 × 1016
Variance1.2227782 × 1035
MonotonicityNot monotonic
2023-07-17T19:16:08.428923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4688422 1
 
< 0.1%
46951493 1
 
< 0.1%
48096183 1
 
< 0.1%
48083329 1
 
< 0.1%
48073457 1
 
< 0.1%
48407995 1
 
< 0.1%
48705346 1
 
< 0.1%
48071723 1
 
< 0.1%
48069317 1
 
< 0.1%
48401232 1
 
< 0.1%
Other values (18438) 18438
99.9%
ValueCountFrequency (%)
61721 1
< 0.1%
67112 1
< 0.1%
71866 1
< 0.1%
87709 1
< 0.1%
105938 1
< 0.1%
118658 1
< 0.1%
135691 1
< 0.1%
154958 1
< 0.1%
166468 1
< 0.1%
199669 1
< 0.1%
ValueCountFrequency (%)
9.07141413 × 10171
< 0.1%
9.071356433 × 10171
< 0.1%
9.070776199 × 10171
< 0.1%
9.06835625 × 10171
< 0.1%
9.067403252 × 10171
< 0.1%
9.066886505 × 10171
< 0.1%
9.066534425 × 10171
< 0.1%
9.066069083 × 10171
< 0.1%
9.06410568 × 10171
< 0.1%
9.06319427 × 10171
< 0.1%

name
Categorical

Distinct13413
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Memory size288.2 KiB
Rental unit in Bondi Beach · 1 bedroom · 1 bed · 1 bath
 
122
Rental unit in Bondi Beach · 2 bedrooms · 2 beds · 1 bath
 
67
Rental unit in Bondi · 1 bedroom · 1 bed · 1 bath
 
63
Rental unit in Surry Hills · 1 bedroom · 1 bed · 1 bath
 
58
Rental unit in Manly · 1 bedroom · 1 bed · 1 bath
 
57
Other values (13408)
18081 

Length

Max length87
Median length78
Mean length58.519243
Min length25

Characters and Unicode

Total characters1079563
Distinct characters79
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11700 ?
Unique (%)63.4%

Sample

1st rowRental unit in Manly · ★4.71 · 2 bedrooms · 3 beds · 1.5 baths
2nd rowRental unit in Manly · ★5.0 · 1 bedroom · 1 bed · 1 bath
3rd rowRental unit in Manly · 1 bedroom · 2 beds · 1 bath
4th rowGuesthouse in Eastwood · 1 bedroom · 1 bed · 1 bath
5th rowRental unit in Manly · 3 bedrooms · 3 beds · 2 baths

Common Values

ValueCountFrequency (%)
Rental unit in Bondi Beach · 1 bedroom · 1 bed · 1 bath 122
 
0.7%
Rental unit in Bondi Beach · 2 bedrooms · 2 beds · 1 bath 67
 
0.4%
Rental unit in Bondi · 1 bedroom · 1 bed · 1 bath 63
 
0.3%
Rental unit in Surry Hills · 1 bedroom · 1 bed · 1 bath 58
 
0.3%
Rental unit in Manly · 1 bedroom · 1 bed · 1 bath 57
 
0.3%
Rental unit in North Bondi · 1 bedroom · 1 bed · 1 bath 53
 
0.3%
Rental unit in Coogee · 1 bedroom · 1 bed · 1 bath 52
 
0.3%
Rental unit in Bondi Beach · 1 bedroom · 1 bed · 1 shared bath 46
 
0.2%
Rental unit in Randwick · 1 bedroom · 1 bed · 1 bath 42
 
0.2%
Rental unit in Bondi · 2 bedrooms · 2 beds · 1 bath 41
 
0.2%
Other values (13403) 17847
96.7%

Length

2023-07-17T19:16:09.380338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
· 65212
24.8%
1 31936
12.1%
in 18448
 
7.0%
2 12984
 
4.9%
bath 12797
 
4.9%
rental 10866
 
4.1%
unit 10866
 
4.1%
bedroom 9806
 
3.7%
bed 9392
 
3.6%
beds 8791
 
3.3%
Other values (773) 71820
27.3%

Most occurring characters

ValueCountFrequency (%)
244560
22.7%
e 74999
 
6.9%
· 65212
 
6.0%
o 57635
 
5.3%
b 55761
 
5.2%
n 53905
 
5.0%
t 53201
 
4.9%
a 49011
 
4.5%
d 47940
 
4.4%
i 42418
 
3.9%
Other values (69) 334921
31.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 621955
57.6%
Space Separator 244560
 
22.7%
Decimal Number 81005
 
7.5%
Other Punctuation 76168
 
7.1%
Uppercase Letter 45533
 
4.2%
Other Symbol 10219
 
0.9%
Dash Punctuation 103
 
< 0.1%
Other Letter 18
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 74999
12.1%
o 57635
9.3%
b 55761
9.0%
n 53905
8.7%
t 53201
8.6%
a 49011
7.9%
d 47940
7.7%
i 42418
 
6.8%
r 34806
 
5.6%
s 33278
 
5.4%
Other values (16) 119001
19.1%
Uppercase Letter
ValueCountFrequency (%)
R 12438
27.3%
H 6435
14.1%
B 5066
11.1%
S 3158
 
6.9%
C 2952
 
6.5%
N 2571
 
5.6%
P 2199
 
4.8%
M 2039
 
4.5%
W 1350
 
3.0%
G 1021
 
2.2%
Other values (15) 6304
13.8%
Decimal Number
ValueCountFrequency (%)
1 33741
41.7%
2 14308
17.7%
4 9449
 
11.7%
3 6116
 
7.6%
5 6023
 
7.4%
0 3721
 
4.6%
7 2169
 
2.7%
8 2100
 
2.6%
6 1991
 
2.5%
9 1387
 
1.7%
Other Letter
ValueCountFrequency (%)
5
27.8%
5
27.8%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
· 65212
85.6%
. 10920
 
14.3%
, 23
 
< 0.1%
/ 13
 
< 0.1%
Space Separator
ValueCountFrequency (%)
244560
100.0%
Other Symbol
ValueCountFrequency (%)
10219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 667488
61.8%
Common 412057
38.2%
Han 18
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 74999
11.2%
o 57635
 
8.6%
b 55761
 
8.4%
n 53905
 
8.1%
t 53201
 
8.0%
a 49011
 
7.3%
d 47940
 
7.2%
i 42418
 
6.4%
r 34806
 
5.2%
s 33278
 
5.0%
Other values (41) 164534
24.6%
Common
ValueCountFrequency (%)
244560
59.4%
· 65212
 
15.8%
1 33741
 
8.2%
2 14308
 
3.5%
. 10920
 
2.7%
10219
 
2.5%
4 9449
 
2.3%
3 6116
 
1.5%
5 6023
 
1.5%
0 3721
 
0.9%
Other values (8) 7788
 
1.9%
Han
ValueCountFrequency (%)
5
27.8%
5
27.8%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1004114
93.0%
None 65212
 
6.0%
Misc Symbols 10219
 
0.9%
CJK 18
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
244560
24.4%
e 74999
 
7.5%
o 57635
 
5.7%
b 55761
 
5.6%
n 53905
 
5.4%
t 53201
 
5.3%
a 49011
 
4.9%
d 47940
 
4.8%
i 42418
 
4.2%
r 34806
 
3.5%
Other values (57) 289878
28.9%
None
ValueCountFrequency (%)
· 65212
100.0%
Misc Symbols
ValueCountFrequency (%)
10219
100.0%
CJK
ValueCountFrequency (%)
5
27.8%
5
27.8%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%
1
 
5.6%

host_id
Real number (ℝ)

Distinct13152
Distinct (%)71.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2794359 × 108
Minimum21741
Maximum5.1826875 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:09.707735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21741
5-th percentile3452873
Q122872668
median66307957
Q31.9515158 × 108
95-th percentile4.5720651 × 108
Maximum5.1826875 × 108
Range5.18247 × 108
Interquartile range (IQR)1.7227891 × 108

Descriptive statistics

Standard deviation1.3711254 × 108
Coefficient of variation (CV)1.071664
Kurtosis0.5357352
Mean1.2794359 × 108
Median Absolute Deviation (MAD)56558594
Skewness1.2380825
Sum2.3603033 × 1012
Variance1.8799849 × 1016
MonotonicityNot monotonic
2023-07-17T19:16:10.025291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
279001183 180
 
1.0%
15739069 141
 
0.8%
288743418 138
 
0.7%
7409213 108
 
0.6%
2450066 83
 
0.4%
301753450 73
 
0.4%
16357713 58
 
0.3%
91961414 55
 
0.3%
102664004 53
 
0.3%
474241925 50
 
0.3%
Other values (13142) 17509
94.9%
ValueCountFrequency (%)
21741 1
< 0.1%
33294 1
< 0.1%
42647 1
< 0.1%
46116 1
< 0.1%
56410 1
< 0.1%
59850 1
< 0.1%
60582 1
< 0.1%
65554 1
< 0.1%
67766 1
< 0.1%
68133 1
< 0.1%
ValueCountFrequency (%)
518268746 1
< 0.1%
518116505 1
< 0.1%
517974856 1
< 0.1%
517665755 1
< 0.1%
517662279 1
< 0.1%
517476727 1
< 0.1%
517432477 1
< 0.1%
517115287 1
< 0.1%
517020437 1
< 0.1%
516935257 1
< 0.1%

host_name
Categorical

Distinct4863
Distinct (%)26.4%
Missing2
Missing (%)< 0.1%
Memory size288.2 KiB
David
 
186
MadeComfy
 
180
Ken
 
158
The Apartment Service
 
141
Tim
 
123
Other values (4858)
17658 

Length

Max length35
Median length32
Mean length6.5353464
Min length1

Characters and Unicode

Total characters120551
Distinct characters219
Distinct categories14 ?
Distinct scripts6 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2924 ?
Unique (%)15.9%

Sample

1st rowAnne
2nd rowJoel
3rd rowSimon
4th rowEbrahim
5th rowGemma

Common Values

ValueCountFrequency (%)
David 186
 
1.0%
MadeComfy 180
 
1.0%
Ken 158
 
0.9%
The Apartment Service 141
 
0.8%
Tim 123
 
0.7%
Michael 114
 
0.6%
James 113
 
0.6%
L'Abode Accommodation Specialist 108
 
0.6%
Sarah 99
 
0.5%
Megan 93
 
0.5%
Other values (4853) 17131
92.9%

Length

2023-07-17T19:16:10.340182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
207
 
1.0%
david 203
 
1.0%
madecomfy 191
 
0.9%
and 187
 
0.9%
the 181
 
0.9%
ken 160
 
0.8%
apartment 149
 
0.7%
service 141
 
0.7%
accommodation 131
 
0.6%
tim 128
 
0.6%
Other values (4686) 19614
92.1%

Most occurring characters

ValueCountFrequency (%)
a 13595
 
11.3%
e 12299
 
10.2%
i 9773
 
8.1%
n 9474
 
7.9%
r 6542
 
5.4%
l 5454
 
4.5%
o 5342
 
4.4%
t 4162
 
3.5%
s 3778
 
3.1%
h 3317
 
2.8%
Other values (209) 46815
38.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 94986
78.8%
Uppercase Letter 21779
 
18.1%
Space Separator 2859
 
2.4%
Other Punctuation 358
 
0.3%
Other Letter 191
 
0.2%
Decimal Number 144
 
0.1%
Dash Punctuation 76
 
0.1%
Open Punctuation 73
 
0.1%
Close Punctuation 73
 
0.1%
Math Symbol 6
 
< 0.1%
Other values (4) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
20
 
10.5%
10
 
5.2%
8
 
4.2%
6
 
3.1%
5
 
2.6%
5
 
2.6%
5
 
2.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (96) 123
64.4%
Lowercase Letter
ValueCountFrequency (%)
a 13595
14.3%
e 12299
12.9%
i 9773
10.3%
n 9474
10.0%
r 6542
 
6.9%
l 5454
 
5.7%
o 5342
 
5.6%
t 4162
 
4.4%
s 3778
 
4.0%
h 3317
 
3.5%
Other values (42) 21250
22.4%
Uppercase Letter
ValueCountFrequency (%)
A 2581
 
11.9%
M 2056
 
9.4%
S 2038
 
9.4%
J 1720
 
7.9%
C 1493
 
6.9%
L 1175
 
5.4%
T 1079
 
5.0%
K 993
 
4.6%
H 981
 
4.5%
R 929
 
4.3%
Other values (22) 6734
30.9%
Decimal Number
ValueCountFrequency (%)
0 39
27.1%
6 35
24.3%
3 35
24.3%
2 20
13.9%
7 5
 
3.5%
5 5
 
3.5%
8 2
 
1.4%
1 2
 
1.4%
9 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
& 197
55.0%
' 110
30.7%
: 18
 
5.0%
. 14
 
3.9%
/ 8
 
2.2%
, 5
 
1.4%
; 4
 
1.1%
@ 2
 
0.6%
Space Separator
ValueCountFrequency (%)
2858
> 99.9%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 3
50.0%
~ 3
50.0%
Final Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 73
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 116759
96.9%
Common 3594
 
3.0%
Han 171
 
0.1%
Hangul 14
 
< 0.1%
Thai 7
 
< 0.1%
Cyrillic 6
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
20
 
11.7%
10
 
5.8%
8
 
4.7%
6
 
3.5%
5
 
2.9%
5
 
2.9%
5
 
2.9%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (79) 103
60.2%
Latin
ValueCountFrequency (%)
a 13595
 
11.6%
e 12299
 
10.5%
i 9773
 
8.4%
n 9474
 
8.1%
r 6542
 
5.6%
l 5454
 
4.7%
o 5342
 
4.6%
t 4162
 
3.6%
s 3778
 
3.2%
h 3317
 
2.8%
Other values (68) 43023
36.8%
Common
ValueCountFrequency (%)
2858
79.5%
& 197
 
5.5%
' 110
 
3.1%
- 76
 
2.1%
( 73
 
2.0%
) 73
 
2.0%
0 39
 
1.1%
6 35
 
1.0%
3 35
 
1.0%
2 20
 
0.6%
Other values (18) 78
 
2.2%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Thai
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Cyrillic
ValueCountFrequency (%)
м 1
16.7%
е 1
16.7%
т 1
16.7%
р 1
16.7%
А 1
16.7%
З 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120238
99.7%
CJK 171
 
0.1%
None 104
 
0.1%
Hangul 14
 
< 0.1%
Thai 7
 
< 0.1%
Cyrillic 6
 
< 0.1%
Punctuation 4
 
< 0.1%
Misc Symbols 2
 
< 0.1%
IPA Ext 2
 
< 0.1%
Phonetic Ext 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 13595
 
11.3%
e 12299
 
10.2%
i 9773
 
8.1%
n 9474
 
7.9%
r 6542
 
5.4%
l 5454
 
4.5%
o 5342
 
4.4%
t 4162
 
3.5%
s 3778
 
3.1%
h 3317
 
2.8%
Other values (65) 46502
38.7%
None
ValueCountFrequency (%)
é 37
35.6%
í 23
22.1%
á 7
 
6.7%
ï 6
 
5.8%
ë 5
 
4.8%
ç 3
 
2.9%
è 3
 
2.9%
ü 2
 
1.9%
â 2
 
1.9%
É 2
 
1.9%
Other values (11) 14
 
13.5%
CJK
ValueCountFrequency (%)
20
 
11.7%
10
 
5.8%
8
 
4.7%
6
 
3.5%
5
 
2.9%
5
 
2.9%
5
 
2.9%
3
 
1.8%
3
 
1.8%
3
 
1.8%
Other values (79) 103
60.2%
Misc Symbols
ValueCountFrequency (%)
2
100.0%
Hangul
ValueCountFrequency (%)
2
14.3%
2
14.3%
2
14.3%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
IPA Ext
ValueCountFrequency (%)
ʜ 1
50.0%
ɴ 1
50.0%
Punctuation
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin Ext Additional
ValueCountFrequency (%)
1
100.0%
Cyrillic
ValueCountFrequency (%)
м 1
16.7%
е 1
16.7%
т 1
16.7%
р 1
16.7%
А 1
16.7%
З 1
16.7%
Phonetic Ext
ValueCountFrequency (%)
1
50.0%
1
50.0%
Thai
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

neighbourhood_group
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing18448
Missing (%)100.0%
Memory size288.2 KiB

neighbourhood
Categorical

Distinct38
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size288.2 KiB
Sydney
4081 
Waverley
2395 
Randwick
1579 
Warringah
927 
Manly
 
815
Other values (33)
8651 

Length

Max length16
Median length15
Mean length8.2439289
Min length4

Characters and Unicode

Total characters152084
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManly
2nd rowManly
3rd rowManly
4th rowRyde
5th rowManly

Common Values

ValueCountFrequency (%)
Sydney 4081
22.1%
Waverley 2395
13.0%
Randwick 1579
 
8.6%
Warringah 927
 
5.0%
Manly 815
 
4.4%
North Sydney 716
 
3.9%
Woollahra 682
 
3.7%
Marrickville 585
 
3.2%
Pittwater 436
 
2.4%
Leichhardt 430
 
2.3%
Other values (28) 5802
31.5%

Length

2023-07-17T19:16:10.676550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sydney 4797
22.8%
waverley 2395
 
11.4%
randwick 1579
 
7.5%
warringah 927
 
4.4%
manly 815
 
3.9%
north 716
 
3.4%
woollahra 682
 
3.2%
bay 655
 
3.1%
marrickville 585
 
2.8%
shire 552
 
2.6%
Other values (35) 7340
34.9%

Most occurring characters

ValueCountFrequency (%)
a 15261
 
10.0%
y 15207
 
10.0%
e 14414
 
9.5%
r 11365
 
7.5%
n 11312
 
7.4%
d 8976
 
5.9%
l 8781
 
5.8%
i 7142
 
4.7%
S 5786
 
3.8%
h 5126
 
3.4%
Other values (30) 48714
32.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127514
83.8%
Uppercase Letter 21509
 
14.1%
Space Separator 2595
 
1.7%
Dash Punctuation 466
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15261
12.0%
y 15207
11.9%
e 14414
11.3%
r 11365
8.9%
n 11312
8.9%
d 8976
 
7.0%
l 8781
 
6.9%
i 7142
 
5.6%
h 5126
 
4.0%
o 5116
 
4.0%
Other values (12) 24814
19.5%
Uppercase Letter
ValueCountFrequency (%)
S 5786
26.9%
W 4302
20.0%
R 2598
12.1%
M 1603
 
7.5%
B 1570
 
7.3%
C 949
 
4.4%
P 894
 
4.2%
H 759
 
3.5%
L 719
 
3.3%
N 716
 
3.3%
Other values (6) 1613
 
7.5%
Space Separator
ValueCountFrequency (%)
2595
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149023
98.0%
Common 3061
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15261
 
10.2%
y 15207
 
10.2%
e 14414
 
9.7%
r 11365
 
7.6%
n 11312
 
7.6%
d 8976
 
6.0%
l 8781
 
5.9%
i 7142
 
4.8%
S 5786
 
3.9%
h 5126
 
3.4%
Other values (28) 45653
30.6%
Common
ValueCountFrequency (%)
2595
84.8%
- 466
 
15.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152084
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15261
 
10.0%
y 15207
 
10.0%
e 14414
 
9.5%
r 11365
 
7.5%
n 11312
 
7.4%
d 8976
 
5.9%
l 8781
 
5.8%
i 7142
 
4.7%
S 5786
 
3.8%
h 5126
 
3.4%
Other values (30) 48714
32.0%

latitude
Real number (ℝ)

Distinct13069
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-33.862176
Minimum-34.10068
Maximum-33.38364
Zeros0
Zeros (%)0.0%
Negative18448
Negative (%)100.0%
Memory size288.2 KiB
2023-07-17T19:16:10.996965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-34.10068
5-th percentile-33.953559
Q1-33.899813
median-33.88144
Q3-33.824727
95-th percentile-33.724781
Maximum-33.38364
Range0.71704
Interquartile range (IQR)0.07508581

Descriptive statistics

Standard deviation0.074203505
Coefficient of variation (CV)-0.0021913389
Kurtosis3.0280169
Mean-33.862176
Median Absolute Deviation (MAD)0.03051
Skewness0.98575803
Sum-624689.43
Variance0.0055061602
MonotonicityNot monotonic
2023-07-17T19:16:11.341973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-33.87907 11
 
0.1%
-33.8703262 11
 
0.1%
-33.88442 8
 
< 0.1%
-33.89442 8
 
< 0.1%
-33.88788 8
 
< 0.1%
-33.89327 8
 
< 0.1%
-33.88842 8
 
< 0.1%
-33.88956 8
 
< 0.1%
-33.89111 8
 
< 0.1%
-33.88686 8
 
< 0.1%
Other values (13059) 18362
99.5%
ValueCountFrequency (%)
-34.10068 1
< 0.1%
-34.09723 1
< 0.1%
-34.09583546 1
< 0.1%
-34.09492127 1
< 0.1%
-34.0933507 1
< 0.1%
-34.09202 1
< 0.1%
-34.09055273 1
< 0.1%
-34.09018 1
< 0.1%
-34.08999 1
< 0.1%
-34.08928 1
< 0.1%
ValueCountFrequency (%)
-33.38364 1
< 0.1%
-33.39315033 1
< 0.1%
-33.40049081 1
< 0.1%
-33.4005 1
< 0.1%
-33.4007165 1
< 0.1%
-33.40116 1
< 0.1%
-33.40141215 1
< 0.1%
-33.40165438 1
< 0.1%
-33.40289 1
< 0.1%
-33.40294706 1
< 0.1%

longitude
Real number (ℝ)

Distinct14087
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.18704
Minimum150.63049
Maximum151.33907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:11.706364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum150.63049
5-th percentile150.98772
Q1151.15303
median151.20987
Q3151.25613
95-th percentile151.28774
Maximum151.33907
Range0.70857614
Interquartile range (IQR)0.10310749

Descriptive statistics

Standard deviation0.099900267
Coefficient of variation (CV)0.00066077268
Kurtosis4.5594871
Mean151.18704
Median Absolute Deviation (MAD)0.049758282
Skewness-1.8622636
Sum2789098.6
Variance0.0099800633
MonotonicityNot monotonic
2023-07-17T19:16:12.069156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151.1968183 11
 
0.1%
151.20961 10
 
0.1%
151.20627 10
 
0.1%
151.20694 9
 
< 0.1%
151.2137 9
 
< 0.1%
151.28153 8
 
< 0.1%
151.20459 7
 
< 0.1%
151.21252 7
 
< 0.1%
151.21417 7
 
< 0.1%
151.20978 7
 
< 0.1%
Other values (14077) 18363
99.5%
ValueCountFrequency (%)
150.63049 1
< 0.1%
150.64493 1
< 0.1%
150.65164 1
< 0.1%
150.65301 1
< 0.1%
150.6600379 1
< 0.1%
150.66007 1
< 0.1%
150.6623881 1
< 0.1%
150.66291 1
< 0.1%
150.66316 1
< 0.1%
150.663324 1
< 0.1%
ValueCountFrequency (%)
151.3390661 1
< 0.1%
151.33877 1
< 0.1%
151.3387 1
< 0.1%
151.33836 1
< 0.1%
151.3381773 1
< 0.1%
151.33812 1
< 0.1%
151.33774 1
< 0.1%
151.33773 1
< 0.1%
151.3375356 1
< 0.1%
151.337494 1
< 0.1%

room_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size288.2 KiB
Entire home/apt
12035 
Private room
6085 
Shared room
 
267
Hotel room
 
61

Length

Max length15
Median length15
Mean length13.936036
Min length10

Characters and Unicode

Total characters257092
Distinct characters19
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEntire home/apt
2nd rowEntire home/apt
3rd rowEntire home/apt
4th rowEntire home/apt
5th rowEntire home/apt

Common Values

ValueCountFrequency (%)
Entire home/apt 12035
65.2%
Private room 6085
33.0%
Shared room 267
 
1.4%
Hotel room 61
 
0.3%

Length

2023-07-17T19:16:12.427050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T19:16:12.741150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
entire 12035
32.6%
home/apt 12035
32.6%
room 6413
17.4%
private 6085
16.5%
shared 267
 
0.7%
hotel 61
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 30483
11.9%
t 30216
11.8%
o 24922
9.7%
r 24800
9.6%
m 18448
 
7.2%
18448
 
7.2%
a 18387
 
7.2%
i 18120
 
7.0%
h 12302
 
4.8%
p 12035
 
4.7%
Other values (9) 48931
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 208161
81.0%
Space Separator 18448
 
7.2%
Uppercase Letter 18448
 
7.2%
Other Punctuation 12035
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30483
14.6%
t 30216
14.5%
o 24922
12.0%
r 24800
11.9%
m 18448
8.9%
a 18387
8.8%
i 18120
8.7%
h 12302
5.9%
p 12035
 
5.8%
n 12035
 
5.8%
Other values (3) 6413
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
E 12035
65.2%
P 6085
33.0%
S 267
 
1.4%
H 61
 
0.3%
Space Separator
ValueCountFrequency (%)
18448
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 12035
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 226609
88.1%
Common 30483
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30483
13.5%
t 30216
13.3%
o 24922
11.0%
r 24800
10.9%
m 18448
8.1%
a 18387
8.1%
i 18120
8.0%
h 12302
5.4%
p 12035
 
5.3%
E 12035
 
5.3%
Other values (7) 24861
11.0%
Common
ValueCountFrequency (%)
18448
60.5%
/ 12035
39.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 257092
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30483
11.9%
t 30216
11.8%
o 24922
9.7%
r 24800
9.6%
m 18448
 
7.2%
18448
 
7.2%
a 18387
 
7.2%
i 18120
 
7.0%
h 12302
 
4.8%
p 12035
 
4.7%
Other values (9) 48931
19.0%

price
Real number (ℝ)

Distinct571
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.14094
Minimum18
Maximum600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:13.003318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile45
Q188
median151
Q3250
95-th percentile464
Maximum600
Range582
Interquartile range (IQR)162

Descriptive statistics

Standard deviation128.26091
Coefficient of variation (CV)0.68537065
Kurtosis0.8657649
Mean187.14094
Median Absolute Deviation (MAD)74
Skewness1.1587823
Sum3452376
Variance16450.86
MonotonicityNot monotonic
2023-07-17T19:16:13.300758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 565
 
3.1%
100 555
 
3.0%
200 469
 
2.5%
250 417
 
2.3%
50 394
 
2.1%
60 368
 
2.0%
70 351
 
1.9%
120 350
 
1.9%
300 343
 
1.9%
80 341
 
1.8%
Other values (561) 14295
77.5%
ValueCountFrequency (%)
18 4
 
< 0.1%
19 4
 
< 0.1%
20 12
0.1%
21 2
 
< 0.1%
22 9
 
< 0.1%
23 4
 
< 0.1%
24 3
 
< 0.1%
25 26
0.1%
26 9
 
< 0.1%
27 7
 
< 0.1%
ValueCountFrequency (%)
600 123
0.7%
599 11
 
0.1%
597 2
 
< 0.1%
596 3
 
< 0.1%
595 8
 
< 0.1%
593 2
 
< 0.1%
592 2
 
< 0.1%
591 4
 
< 0.1%
590 15
 
0.1%
589 2
 
< 0.1%

minimum_nights
Real number (ℝ)

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.947853
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:13.600062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median90
Q390
95-th percentile90
Maximum90
Range89
Interquartile range (IQR)87

Descriptive statistics

Standard deviation42.573702
Coefficient of variation (CV)0.80406851
Kurtosis-1.8913261
Mean52.947853
Median Absolute Deviation (MAD)0
Skewness-0.29436489
Sum976782
Variance1812.5201
MonotonicityNot monotonic
2023-07-17T19:16:13.894058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
90 10433
56.6%
2 2538
 
13.8%
1 1725
 
9.4%
3 1340
 
7.3%
4 555
 
3.0%
5 517
 
2.8%
7 500
 
2.7%
28 233
 
1.3%
14 145
 
0.8%
21 102
 
0.6%
Other values (31) 360
 
2.0%
ValueCountFrequency (%)
1 1725
9.4%
2 2538
13.8%
3 1340
7.3%
4 555
 
3.0%
5 517
 
2.8%
6 81
 
0.4%
7 500
 
2.7%
8 10
 
0.1%
9 4
 
< 0.1%
10 71
 
0.4%
ValueCountFrequency (%)
90 10433
56.6%
89 5
 
< 0.1%
85 1
 
< 0.1%
84 4
 
< 0.1%
80 1
 
< 0.1%
70 2
 
< 0.1%
65 2
 
< 0.1%
60 30
 
0.2%
59 1
 
< 0.1%
50 1
 
< 0.1%

number_of_reviews
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7647441
Minimum0
Maximum50
Zeros4995
Zeros (%)27.1%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:14.202496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310
95-th percentile35
Maximum50
Range50
Interquartile range (IQR)10

Descriptive statistics

Standard deviation11.312058
Coefficient of variation (CV)1.4568487
Kurtosis2.7305088
Mean7.7647441
Median Absolute Deviation (MAD)2
Skewness1.8546245
Sum143244
Variance127.96265
MonotonicityNot monotonic
2023-07-17T19:16:14.550299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4995
27.1%
1 2730
14.8%
2 1561
 
8.5%
3 1130
 
6.1%
4 821
 
4.5%
5 642
 
3.5%
6 485
 
2.6%
8 452
 
2.5%
7 447
 
2.4%
10 332
 
1.8%
Other values (41) 4853
26.3%
ValueCountFrequency (%)
0 4995
27.1%
1 2730
14.8%
2 1561
 
8.5%
3 1130
 
6.1%
4 821
 
4.5%
5 642
 
3.5%
6 485
 
2.6%
7 447
 
2.4%
8 452
 
2.5%
9 324
 
1.8%
ValueCountFrequency (%)
50 48
0.3%
49 40
0.2%
48 57
0.3%
47 53
0.3%
46 56
0.3%
45 57
0.3%
44 40
0.2%
43 61
0.3%
42 58
0.3%
41 60
0.3%

last_review
Categorical

HIGH CARDINALITY  MISSING 

Distinct2379
Distinct (%)17.7%
Missing4995
Missing (%)27.1%
Memory size288.2 KiB
2023-06-04
 
213
2023-05-21
 
213
2023-05-28
 
204
2023-05-20
 
148
2023-05-22
 
136
Other values (2374)
12539 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters134530
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique743 ?
Unique (%)5.5%

Sample

1st row2023-01-28
2nd row2019-12-12
3rd row2018-01-06
4th row2023-05-31
5th row2014-10-31

Common Values

ValueCountFrequency (%)
2023-06-04 213
 
1.2%
2023-05-21 213
 
1.2%
2023-05-28 204
 
1.1%
2023-05-20 148
 
0.8%
2023-05-22 136
 
0.7%
2023-05-07 136
 
0.7%
2023-05-29 120
 
0.7%
2023-06-03 110
 
0.6%
2023-05-27 105
 
0.6%
2023-05-14 105
 
0.6%
Other values (2369) 11963
64.8%
(Missing) 4995
27.1%

Length

2023-07-17T19:16:14.894980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2023-06-04 213
 
1.6%
2023-05-21 213
 
1.6%
2023-05-28 204
 
1.5%
2023-05-20 148
 
1.1%
2023-05-22 136
 
1.0%
2023-05-07 136
 
1.0%
2023-05-29 120
 
0.9%
2023-06-03 110
 
0.8%
2023-05-27 105
 
0.8%
2023-05-14 105
 
0.8%
Other values (2369) 11963
88.9%

Most occurring characters

ValueCountFrequency (%)
0 32943
24.5%
2 30451
22.6%
- 26906
20.0%
1 15561
11.6%
3 9509
 
7.1%
5 4527
 
3.4%
9 3141
 
2.3%
4 3135
 
2.3%
6 2922
 
2.2%
8 2888
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107624
80.0%
Dash Punctuation 26906
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32943
30.6%
2 30451
28.3%
1 15561
14.5%
3 9509
 
8.8%
5 4527
 
4.2%
9 3141
 
2.9%
4 3135
 
2.9%
6 2922
 
2.7%
8 2888
 
2.7%
7 2547
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 26906
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 134530
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32943
24.5%
2 30451
22.6%
- 26906
20.0%
1 15561
11.6%
3 9509
 
7.1%
5 4527
 
3.4%
9 3141
 
2.3%
4 3135
 
2.3%
6 2922
 
2.2%
8 2888
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32943
24.5%
2 30451
22.6%
- 26906
20.0%
1 15561
11.6%
3 9509
 
7.1%
5 4527
 
3.4%
9 3141
 
2.3%
4 3135
 
2.3%
6 2922
 
2.2%
8 2888
 
2.1%

reviews_per_month
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct565
Distinct (%)4.2%
Missing4995
Missing (%)27.1%
Infinite0
Infinite (%)0.0%
Mean0.7305456
Minimum0.01
Maximum13.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:15.198598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.05
median0.23
Q30.99
95-th percentile3
Maximum13.58
Range13.57
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation1.1213069
Coefficient of variation (CV)1.5348897
Kurtosis11.602099
Mean0.7305456
Median Absolute Deviation (MAD)0.21
Skewness2.7905134
Sum9828.03
Variance1.2573292
MonotonicityNot monotonic
2023-07-17T19:16:15.580922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.02 1379
 
7.5%
0.01 744
 
4.0%
0.03 620
 
3.4%
0.04 550
 
3.0%
0.05 476
 
2.6%
0.06 360
 
2.0%
0.07 351
 
1.9%
1 225
 
1.2%
0.08 223
 
1.2%
0.09 208
 
1.1%
Other values (555) 8317
45.1%
(Missing) 4995
27.1%
ValueCountFrequency (%)
0.01 744
4.0%
0.02 1379
7.5%
0.03 620
3.4%
0.04 550
 
3.0%
0.05 476
 
2.6%
0.06 360
 
2.0%
0.07 351
 
1.9%
0.08 223
 
1.2%
0.09 208
 
1.1%
0.1 195
 
1.1%
ValueCountFrequency (%)
13.58 1
< 0.1%
13.47 1
< 0.1%
10.78 1
< 0.1%
10.14 1
< 0.1%
10 1
< 0.1%
9.6 1
< 0.1%
9.47 1
< 0.1%
9.38 1
< 0.1%
9.31 1
< 0.1%
9.09 1
< 0.1%
Distinct57
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.252385
Minimum1
Maximum229
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:15.897852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile56
Maximum229
Range228
Interquartile range (IQR)2

Descriptive statistics

Standard deviation33.554656
Coefficient of variation (CV)2.9820039
Kurtosis22.649521
Mean11.252385
Median Absolute Deviation (MAD)0
Skewness4.6335124
Sum207584
Variance1125.9149
MonotonicityNot monotonic
2023-07-17T19:16:16.207935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11161
60.5%
2 2138
 
11.6%
3 750
 
4.1%
4 440
 
2.4%
5 286
 
1.6%
7 246
 
1.3%
6 210
 
1.1%
229 180
 
1.0%
145 141
 
0.8%
10 141
 
0.8%
Other values (47) 2755
 
14.9%
ValueCountFrequency (%)
1 11161
60.5%
2 2138
 
11.6%
3 750
 
4.1%
4 440
 
2.4%
5 286
 
1.6%
6 210
 
1.1%
7 246
 
1.3%
8 136
 
0.7%
9 121
 
0.7%
10 141
 
0.8%
ValueCountFrequency (%)
229 180
1.0%
176 108
0.6%
155 138
0.7%
145 141
0.8%
102 58
 
0.3%
96 83
0.4%
80 1
 
< 0.1%
78 73
0.4%
64 55
 
0.3%
57 40
 
0.2%

availability_365
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct366
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.736882
Minimum0
Maximum365
Zeros8708
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:16.539636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20
Q3167
95-th percentile363
Maximum365
Range365
Interquartile range (IQR)167

Descriptive statistics

Standard deviation124.32439
Coefficient of variation (CV)1.3123125
Kurtosis-0.23182098
Mean94.736882
Median Absolute Deviation (MAD)20
Skewness1.0935197
Sum1747706
Variance15456.555
MonotonicityNot monotonic
2023-07-17T19:16:16.944713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8708
47.2%
90 530
 
2.9%
365 527
 
2.9%
364 351
 
1.9%
89 240
 
1.3%
179 214
 
1.2%
180 157
 
0.9%
363 119
 
0.6%
84 114
 
0.6%
91 106
 
0.6%
Other values (356) 7382
40.0%
ValueCountFrequency (%)
0 8708
47.2%
1 57
 
0.3%
2 30
 
0.2%
3 39
 
0.2%
4 40
 
0.2%
5 35
 
0.2%
6 20
 
0.1%
7 13
 
0.1%
8 26
 
0.1%
9 20
 
0.1%
ValueCountFrequency (%)
365 527
2.9%
364 351
1.9%
363 119
 
0.6%
362 80
 
0.4%
361 36
 
0.2%
360 40
 
0.2%
359 26
 
0.1%
358 78
 
0.4%
357 28
 
0.2%
356 33
 
0.2%

number_of_reviews_ltm
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct51
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4599415
Minimum0
Maximum50
Zeros11975
Zeros (%)64.9%
Negative0
Negative (%)0.0%
Memory size288.2 KiB
2023-07-17T19:16:17.453385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile20
Maximum50
Range50
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.2846708
Coefficient of variation (CV)2.1054318
Kurtosis8.5540453
Mean3.4599415
Median Absolute Deviation (MAD)0
Skewness2.7946285
Sum63829
Variance53.066428
MonotonicityNot monotonic
2023-07-17T19:16:17.978286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11975
64.9%
1 912
 
4.9%
2 665
 
3.6%
3 498
 
2.7%
4 415
 
2.2%
5 368
 
2.0%
6 295
 
1.6%
7 289
 
1.6%
8 289
 
1.6%
9 252
 
1.4%
Other values (41) 2490
 
13.5%
ValueCountFrequency (%)
0 11975
64.9%
1 912
 
4.9%
2 665
 
3.6%
3 498
 
2.7%
4 415
 
2.2%
5 368
 
2.0%
6 295
 
1.6%
7 289
 
1.6%
8 289
 
1.6%
9 252
 
1.4%
ValueCountFrequency (%)
50 4
 
< 0.1%
49 7
< 0.1%
48 4
 
< 0.1%
47 6
< 0.1%
46 8
< 0.1%
45 8
< 0.1%
44 4
 
< 0.1%
43 13
0.1%
42 5
 
< 0.1%
41 11
0.1%

license
Categorical

HIGH CARDINALITY  MISSING 

Distinct5510
Distinct (%)74.9%
Missing11094
Missing (%)60.1%
Memory size288.2 KiB
Exempt
1390 
PID-STRA-27027
 
25
PID-STRA-49937
 
22
PID-STRA-38401
 
13
A.B.N.57 002 964 669
 
10
Other values (5505)
5894 

Length

Max length20
Median length14
Mean length12.496872
Min length6

Characters and Unicode

Total characters91902
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5242 ?
Unique (%)71.3%

Sample

1st rowPID-STRA-26284
2nd rowPID-STRA-26166
3rd rowPID-STRA-51614
4th rowPID-STRA-45916
5th rowPID-STRA-49889

Common Values

ValueCountFrequency (%)
Exempt 1390
 
7.5%
PID-STRA-27027 25
 
0.1%
PID-STRA-49937 22
 
0.1%
PID-STRA-38401 13
 
0.1%
A.B.N.57 002 964 669 10
 
0.1%
PID-STRA-44538 10
 
0.1%
PID-STRA-6133-1 10
 
0.1%
PID-STRA-41992 10
 
0.1%
PID-STRA-1237 10
 
0.1%
PID-STRA-29404 9
 
< 0.1%
Other values (5500) 5845
31.7%
(Missing) 11094
60.1%

Length

2023-07-17T19:16:18.484709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
exempt 1390
 
18.8%
pid-stra-27027 25
 
0.3%
pid-stra-49937 22
 
0.3%
pid-stra-38401 13
 
0.2%
a.b.n.57 10
 
0.1%
002 10
 
0.1%
964 10
 
0.1%
669 10
 
0.1%
pid-stra-44538 10
 
0.1%
pid-stra-6133-1 10
 
0.1%
Other values (5503) 5874
79.6%

Most occurring characters

ValueCountFrequency (%)
- 12202
13.3%
A 5964
 
6.5%
P 5954
 
6.5%
I 5954
 
6.5%
D 5954
 
6.5%
S 5954
 
6.5%
T 5954
 
6.5%
R 5954
 
6.5%
4 4374
 
4.8%
3 3542
 
3.9%
Other values (18) 30096
32.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 43098
46.9%
Decimal Number 29592
32.2%
Dash Punctuation 12202
 
13.3%
Lowercase Letter 6950
 
7.6%
Other Punctuation 30
 
< 0.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 5964
13.8%
P 5954
13.8%
I 5954
13.8%
D 5954
13.8%
S 5954
13.8%
T 5954
13.8%
R 5954
13.8%
E 1390
 
3.2%
B 10
 
< 0.1%
N 10
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 4374
14.8%
3 3542
12.0%
1 3299
11.1%
2 3241
11.0%
5 3016
10.2%
9 2508
8.5%
7 2470
8.3%
6 2392
8.1%
0 2386
8.1%
8 2364
8.0%
Lowercase Letter
ValueCountFrequency (%)
x 1390
20.0%
t 1390
20.0%
p 1390
20.0%
m 1390
20.0%
e 1390
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 12202
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50048
54.5%
Common 41854
45.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 5964
11.9%
P 5954
11.9%
I 5954
11.9%
D 5954
11.9%
S 5954
11.9%
T 5954
11.9%
R 5954
11.9%
x 1390
 
2.8%
t 1390
 
2.8%
p 1390
 
2.8%
Other values (5) 4190
8.4%
Common
ValueCountFrequency (%)
- 12202
29.2%
4 4374
 
10.5%
3 3542
 
8.5%
1 3299
 
7.9%
2 3241
 
7.7%
5 3016
 
7.2%
9 2508
 
6.0%
7 2470
 
5.9%
6 2392
 
5.7%
0 2386
 
5.7%
Other values (3) 2424
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91902
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 12202
13.3%
A 5964
 
6.5%
P 5954
 
6.5%
I 5954
 
6.5%
D 5954
 
6.5%
S 5954
 
6.5%
T 5954
 
6.5%
R 5954
 
6.5%
4 4374
 
4.8%
3 3542
 
3.9%
Other values (18) 30096
32.7%

Interactions

2023-07-17T19:16:02.008573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:25.261153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:28.346267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:31.736973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:36.029207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:39.782861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:42.936773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:48.264418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:52.583050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:55.941473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:58.856243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:02.378008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:25.518166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:28.927749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:32.023574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:36.410869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:40.066586image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:43.176193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:48.584712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:52.873302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:56.176393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:59.090262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:02.842254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:25.788200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:29.199471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:32.327428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:36.810496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:40.341290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:43.423648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:48.921669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:53.162773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:56.483049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:59.364961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:03.304640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:26.080709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:29.512271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:32.798772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:37.131035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:40.631129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:44.301076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:49.409523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:53.472297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:56.792501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:59.678543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:03.728792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:26.391046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:29.817680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:33.269472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:37.392471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:40.937994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:44.667559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:49.825666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:53.784604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:57.092193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:59.992099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:04.064631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:26.659036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:30.120807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:33.672891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:37.661960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:41.234144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:45.394743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:50.244606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:54.067727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:57.340106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:00.274776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:04.426810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:26.933654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:30.403637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:34.070312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:37.931664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:41.515752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:45.787152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:50.632458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:54.649971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:57.584820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:00.549207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:04.826046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:27.203041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:30.672651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:34.449823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:38.214961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:41.800083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:46.094927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:51.013595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:54.902227image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:57.831475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:00.858265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:05.250188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:27.484411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:30.931874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:34.850902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:38.513331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:42.093605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:46.914662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:51.469075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:55.159302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:58.094115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:01.162576image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:05.651222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:27.760552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:31.166239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:35.196215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:38.809816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:42.363649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:47.334098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:51.921637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:55.428402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:58.321284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:01.433399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:06.029445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:28.068857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:31.450658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:35.635076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:39.486457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:42.667442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:47.799673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:52.284148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:55.686170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:15:58.605044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T19:16:01.735469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-17T19:16:18.831016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
idhost_idlatitudelongitudepriceminimum_nightsnumber_of_reviewsreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmneighbourhoodroom_type
id1.0000.4380.085-0.1600.253-0.6530.0360.7050.3680.4540.4820.0990.144
host_id0.4381.0000.053-0.295-0.017-0.2600.0350.3070.2160.2210.2050.1340.114
latitude0.0850.0531.0000.0570.148-0.1240.0460.1000.0470.1160.0900.6340.075
longitude-0.160-0.2950.0571.0000.2400.109-0.049-0.151-0.157-0.159-0.0850.7150.112
price0.253-0.0170.1480.2401.000-0.3770.1190.3030.1320.2690.3110.1130.405
minimum_nights-0.653-0.260-0.1240.109-0.3771.000-0.367-0.760-0.417-0.525-0.7710.0880.176
number_of_reviews0.0360.0350.046-0.0490.119-0.3671.0000.6820.1980.1950.6300.0440.108
reviews_per_month0.7050.3070.100-0.1510.303-0.7600.6821.0000.3790.5270.8380.0700.086
calculated_host_listings_count0.3680.2160.047-0.1570.132-0.4170.1980.3791.0000.3280.3690.1440.113
availability_3650.4540.2210.116-0.1590.269-0.5250.1950.5270.3281.0000.4740.1060.128
number_of_reviews_ltm0.4820.2050.090-0.0850.311-0.7710.6300.8380.3690.4741.0000.0540.102
neighbourhood0.0990.1340.6340.7150.1130.0880.0440.0700.1440.1060.0541.0000.146
room_type0.1440.1140.0750.1120.4050.1760.1080.0860.1130.1280.1020.1461.000

Missing values

2023-07-17T19:16:06.589052image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T19:16:07.240858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-17T19:16:07.718251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
04688422Rental unit in Manly · ★4.71 · 2 bedrooms · 3 beds · 1.5 baths13396297AnneNaNManly-33.80560151.28433Entire home/apt3001362023-01-280.3513113PID-STRA-26284
139681254Rental unit in Manly · ★5.0 · 1 bedroom · 1 bed · 1 bath25316226JoelNaNManly-33.80512151.28907Entire home/apt1109032019-12-120.07100NaN
222314790Rental unit in Manly · 1 bedroom · 2 beds · 1 bath48897764SimonNaNManly-33.79627151.28476Entire home/apt1909012018-01-060.02100NaN
339446599Guesthouse in Eastwood · 1 bedroom · 1 bed · 1 bath152329169EbrahimNaNRyde-33.78597151.09255Entire home/apt70900NaNNaN100PID-STRA-26166
643619457Rental unit in Manly · 3 bedrooms · 3 beds · 2 baths26816558GemmaNaNManly-33.79515151.28498Entire home/apt560900NaNNaN200NaN
7892848682006777431Cabin in Queenscliff · ★New · Studio · 1 bed · 1 bath514356697JustineNaNManly-33.78488151.28432Entire home/apt24920NaNNaN12390PID-STRA-51614
844067842Home in Fairlight · 3 bedrooms · 2 beds · 2.5 baths4170245IrmkeNaNManly-33.79209151.27732Entire home/apt23430NaNNaN3540PID-STRA-45916
9845977275219873014Rental unit in West Ryde · ★4.67 · 1 bedroom · 1 bed · 1 bath4860812KirriliNaNRyde-33.81324151.08791Entire home/apt120192023-05-313.6511189PID-STRA-49889
13363237Rental unit in Summer Hill · ★4.67 · 1 bedroom · 1 bed · 1 bath1835730MinaNaNAshfield-33.88749151.14016Entire home/apt859032014-10-310.03100NaN
2461721Rental unit in Bondi Beach · ★4.42 · 2 bedrooms · 2 beds · 1 bath299170EilishNaNWaverley-33.88905151.27653Entire home/apt24590332021-04-070.2213090NaN
idnamehost_idhost_nameneighbourhood_groupneighbourhoodlatitudelongituderoom_typepriceminimum_nightsnumber_of_reviewslast_reviewreviews_per_monthcalculated_host_listings_countavailability_365number_of_reviews_ltmlicense
23438906319427002029747Guesthouse in Edmondson Park · ★New · 2 bedrooms · 2 beds · 1 bath514670320AbduNaNLiverpool-33.954348150.862190Entire home/apt16010NaNNaN13640Exempt
23439901396902037093809Camper/RV in Canley Heights · ★New · 1 bedroom · 2 beds · 1 bath304795574Huong Thien NguyenNaNFairfield-33.880299150.918334Private room4610NaNNaN143620Exempt
23440906410567979085806Rental unit in Waterloo · ★New · 1 bedroom · 1 bed · 1 bath324511820DongNaNSydney-33.900515151.213841Entire home/apt126210NaNNaN191680Exempt
23441901624777811095209Rental unit in Arncliffe · ★New · 1 bedroom · 2 beds · 1 bath468475474BoNaNRockdale-33.937568151.148187Entire home/apt17710NaNNaN2890PID-STRA-52346
23442906606908279819571Bed and breakfast in Avalon Beach · ★New · 1 bedroom · 11 beds · 1.5 shared baths72844643Oliver & VerenaNaNPittwater-33.643023151.326208Private room23910NaNNaN32970PID-STRA-4020-2
23443906653442464491046Rental unit in Manly · ★New · 1 bedroom · 1 bed · 1 bath40340765LisaNaNManly-33.801220151.287630Entire home/apt152210NaNNaN2890PID-STRA-35713
23444906688650475677230Home in Rozelle · ★New · 2 bedrooms · 3 beds · 1 bath38478183Guest RealtyNaNLeichhardt-33.867070151.166530Entire home/apt34720NaNNaN353560PID-STRA-52428
23446906740325185303171Home in Banksmeadow · ★New · 1 bedroom · 1 bed · 1 bath518268746CherilynnNaNBotany Bay-33.941845151.211888Private room9910NaNNaN13640PID-STRA-39654
23449906835624973815512Rental unit in Wentworth Point · ★New · 2 bedrooms · 3 beds · 2 baths474434658Short Term Accommodation ManagementNaNAuburn-33.825167151.078528Entire home/apt24030NaNNaN500PID-STRA-52734
23450907077619932231671Home in Millers Point · ★New · 2 bedrooms · 2 beds · 1 bath510052112HaihaoNaNSydney-33.875670151.204620Entire home/apt19910NaNNaN11480Exempt